Conference Agenda

CONTEXT

The impact of Artificial Intelligence (AI) on education, is a matter of discussion across a wide range of fields since the widespread public dissemination of the generative AI-based tools, such as ChatGPT, designed to simulate a real conversation using natural language based on the "Generative Pre-trained Transformer" technique [1]. Even before the advent of "ChatGPT," there were numerous precedents for the application of AI techniques in education, such as "Learning Analytics" and "Adaptive Learning" [2], as well as others for proctoring, plagiarism detection, automated tutoring, etc.

As with previous technological advancements such as radio, television, electronic calculators, computers, and the Internet, the introduction of new technological tools consistently generates debate about their role in education. The application of AI in education is not the exception to this rule. This article describes a study aiming to gather evidence regarding the opportunities and challenges for its effective use in Engineering Education courses by analysing Argentine teachers’ perceptions. Conducting rigorous research and experimentation we will identify effective approaches for applying AI to benefit both teachers and students, with the aim of enhancing education, facilitating learning, and promoting greater inclusion.

Perspectives on this issue are diverse, some studies have dealt with other challenges that AI poses to education and Engineering Education. Some [3] make recommendations regarding the ethical implications of using generative AI-based tools in Higher Education. Others explore the perceptions and attitudes of teachers and students towards these technologies and their impact on teaching and learning practices [4] [5].

PURPOSE OR GOAL

This paper constitutes a segment of an ongoing Research Project developed at the National Technological University (Buenos Aires School) and the Center for Educational Research and Innovation (CIIE) in Argentina. The primary purpose of this paper is to disseminate the initial findings of an in-depth analysis conducted among Argentine engineering educators, with the aim of identifying their perceptions about the use of Artificial Intelligence applications (AIA) in undergraduate courses. To this end, the main objective of this research is to analyse educators' opinions and concerns regarding the use of AIA in their courses, while also identifying critical areas for upskilling to facilitate the effective integration of these tools, ultimately enhancing student learning outcomes.

This study provides a succinct description of the impact of AIA at the National Technological University (Buenos Aires School) and a compilation of AIA survey studies run at Latin American universities aiming to investigate the faculty’s opinions about the educational use of these applications in STEM undergraduate courses. It delineates the research methodology employed, elucidates the instruments crafted specifically for this study, and describes the research process used to collect and organize data. Furthermore, this study highlights preliminary evidence regarding the opportunities and challenges faced by faculty in incorporating these applications. This paper aspires to serve as a reference for future investigations addressing similar topics and to provide insights that will inform subsequent research within the broader framework of the ongoing project.

This paper presents the perspective of chemistry students of the CBC of the University of Buenos Aires, using a virtual assistant integrated in the Remote Laboratory of Acid-Base Titration II. The integration of this artificial intelligence tool arose due to the lack of teaching support for learning in virtual environments such as Remote Laboratories. The aim of this study is to investigate students' perceptions of their experience with a virtual assistant integrated into a remote acid-base titration laboratory. A questionnaire was designed and administered for data collection. It consisted of 11 Likert-type statements, the statements were linked to an agreement level of 1 to 4, where 1 indicated strongly disagree and 4 strongly agree. These statements focused on assessing the usability of the virtual assistant and students' perceptions of their own learning experience when using the tool. In addition, three open-ended questions were included to collect qualitative information aimed at identifying areas for future improvement.

Students indicated that the virtual assistant provided valuable support during the experimental activity, facilitating the immediate resolution of any queries they had. The study reveals a consensus among participants that tools such as the one investigated could be beneficial in improving student performance. This article provides a clear overview of the use of an Artificial Intelligence tool to support work in an ultra-concurrent laboratory in the field of chemistry. In addition, the virtual assistant's ability to provide assistance to students in resolving specific queries prevents difficulties that might impede the progression of experimental activities.

This article is published as part of the KICK 4.0 project, which aims to successfully integrate NLP AI into university engineering education. The focus is on acquiring new skills required for dealing with AI and recognizing the potentials and limitations of AI technologies.

The aim of the study is to investigate the creation of feedback from NLP AI that is conducive to learning. It examines how feedback can be generated that is correct in terms of content and motivating to the right extent, and how this feedback can be generated in a comprehensible and transparent manner.

This work presents a tool that allows a course instructor to design adaptive learning scenarios for developing STEM competencies. Additionally, this environment features an integrated intelligent tutor, represented by a Conversational Agent, which is capable of guiding the student through a personalized learning path tailored to their individual skills and needs

CONTEXT

Artificial Intelligence (AI) has been widely discussed in the technological and research context for a long time. With the rising of applications like ChatGPT it has become more and more relevant for the educational sector as well and is often named as one of the key enabling technologies of our time (Ramahandry et al., 2021).

PURPOSE

Because of its various applications and broad disruptive potential, it seems pivotal to integrate AI into teacher education. The project “LehrKraft voraus!” (“Teacher Ahead!”) aims to foster AI-related and self-reflectional competencies of pre-service technology teachers regarding the use of AI.

APPROACH

In order to achieve this objective, an investigation is first conducted to ascertain the understanding of pre-service technology teachers regarding AI, their attitudes toward the technology and their need for support in using AI in an educational context. To this end, an open-ended questionnaire is employed, utilising adaptations of both the Technology Acceptance Model 3 (Venkatesh & Bala, 2008) and the PANAS scales (Leue & Beauducel, 2011) with a sample of pre-service technology teachers (n=17). This process provides the basis for the development of a practical and holistic seminar, whereby pre-service technology teachers develop learning situations for vocational schools and put these into practice at a cooperating school. The seminar's structure is following the DigCompEdu framework and its extensions towards AI (Bekiaridis & Attwell, 2024; European Commission: Joint Research Centre, Redecker & Punie, 2017) regarding digitalization and AI related competencies. Additionally, we draw upon the work of Dewey (1910) to foster reflection and critical thinking concerning the use of AI in an educational context.

OUTCOMES

The survey of students shows that they mostly have a basic understanding of AI at the application level. This is combined with misconceptions on AI. Despite these, all participants display a positive attitude towards using AI tools in a school context. A large proportion of respondents are already using AI tools, with a particular focus on chatbots but report having problems using them. There is also a clear desire for more support from the university.

In the resulting seminar, student teachers are required to utilise AI tools to support them in the development of the learning situations. Secondly, the content of the learning situations developed also encompasses applications of AI. The learning situations are then implemented at an actual vocational school. The usage of AI is then critically reflected on in the seminar from a subject-specific and a didactical point of view. This approach facilitates the promotion of both the digitalisation and AI-related skills of the prospective teachers and those of the students at the vocational schools.

CONCLUSIONS

The resulting seminar poses a suitable way of integrating AI in vocational teacher education. As the preliminary study indicates teacher students currently possess only a rudimentary understanding of AI and require further assistance in order to utilise it effectively in their teaching profession. The incorporation of AI into the process of developing and enacting learning situations enables the addressing of AI-related competencies, particularly through critical reflection from multiple perspectives.